Defending against Model Stealing via Verifying Embedded External Features

Overview

Defending against Model Stealing Attacks via Verifying Embedded External Features

This is the official implementation of our paper Defending against Model Stealing Attacks via Verifying Embedded External Features, accepted by the AAAI Conference on Artificial Intelligence (AAAI), 2022. This research project is developed based on Python 3 and Pytorch, created by Yiming Li and Linghui Zhu.

Pipeline

Pipeline

Requirements

To install requirements:

pip install -r requirements.txt

Make sure the directory follows:

stealingverification
├── data
│   ├── cifar10
│   └── ...
├── gradients_set 
│   
├── prob
│   
├── network
│   
├── model
│   ├── victim
│   └── ...
|

Dataset Preparation

Make sure the directory data follows:

data
├── cifar10_seurat_10%
|   ├── train
│   └── test
├── cifar10  
│   ├── train
│   └── test
├── subimage_seurat_10%
│   ├── train
|   ├── val
│   └── test
├── sub-imagenet-20
│   ├── train
|   ├── val
│   └── test

📋 Data Download Link:
data

Model Preparation

Make sure the directory model follows:

model
├── victim
│   ├── vict-wrn28-10.pt
│   └── ...
├── benign
│   ├── benign-wrn28-10.pt
│   └── ...
├── attack
│   ├── atta-label-wrn16-1.pt
│   └── ...
└── clf

📋 Model Download Link:
model

Collecting Gradient Vectors

Collect gradient vectors of victim and benign model with respect to transformed images.

CIFAR-10:

python gradientset.py --model=wrn16-1 --m=./model/victim/vict-wrn16-1.pt --dataset=cifar10 --gpu=0
python gradientset.py --model=wrn28-10 --m=./model/victim/vict-wrn28-10.pt --dataset=cifar10 --gpu=0
python gradientset.py --model=wrn16-1 --m=./model/benign/benign-wrn16-1.pt --dataset=cifar10 --gpu=0
python gradientset.py --model=wrn28-10 --m=./model/benign/benign-wrn28-10.pt --dataset=cifar10 --gpu=0

ImageNet:

python gradientset.py --model=resnet34-imgnet --m=./model/victim/vict-imgnet-resnet34.pt --dataset=imagenet --gpu=0
python gradientset.py --model=resnet18-imgnet --m=./model/victim/vict-imgnet-resnet18.pt --dataset=imagenet --gpu=0
python gradientset.py --model=resnet34-imgnet --m=./model/benign/benign-imgnet-resnet34.pt --dataset=imagenet --gpu=0
python gradientset.py --model=resnet18-imgnet --m=./model/benign/benign-imgnet-resnet18.pt --dataset=imagenet --gpu=0

Training Ownership Meta-Classifier

To train the ownership meta-classifier in the paper, run these commands:

CIFAR-10:

python train_clf.py --type=wrn28-10 --dataset=cifar10 --gpu=0
python train_clf.py --type=wrn16-1 --dataset=cifar10 --gpu=0

ImageNet:

python train_clf.py --type=resnet34-imgnet --dataset=imagenet --gpu=0
python train_clf.py --type=resnet18-imgnet --dataset=imagenet --gpu=0

Ownership Verification

To verify the ownership of the suspicious models, run this command:

CIFAR-10:

python ownership_verification.py --mode=source --dataset=cifar10 --gpu=0 

#mode: ['source','distillation','zero-shot','fine-tune','label-query','logit-query','benign']

ImageNet:

python ownership_verification.py --mode=logit-query --dataset=imagenet --gpu=0 

#mode: ['source','distillation','zero-shot','fine-tune','label-query','logit-query','benign']

An Example of the Result

python ownership_verification.py --mode=fine-tune --dataset=cifar10 --gpu=0 

result:  p-val: 1.9594572166549425e-08 mu: 0.47074130177497864

Reference

If our work or this repo is useful for your research, please cite our paper as follows:

@inproceedings{li2022defending,
  title={Defending against Model Stealing via Verifying Embedded External Features},
  author={Li, Yiming and Zhu, Linghui and Jia, Xiaojun and Jiang, Yong and Xia, Shu-Tao and Cao, Xiaochun},
  booktitle={AAAI},
  year={2022}
}
Official source code of Fast Point Transformer, CVPR 2022

Fast Point Transformer Project Page | Paper This repository contains the official source code and data for our paper: Fast Point Transformer Chunghyun

182 Dec 23, 2022
K-Means Clustering and Hierarchical Clustering Unsupervised Learning Solution in Python3.

Unsupervised Learning - K-Means Clustering and Hierarchical Clustering - The Heritage Foundation's Economic Freedom Index Analysis 2019 - By David Sal

David Salako 1 Jan 12, 2022
A tensorflow implementation of GCN-LPA

GCN-LPA This repository is the implementation of GCN-LPA (arXiv): Unifying Graph Convolutional Neural Networks and Label Propagation Hongwei Wang, Jur

Hongwei Wang 83 Nov 28, 2022
Code and model benchmarks for "SEVIR : A Storm Event Imagery Dataset for Deep Learning Applications in Radar and Satellite Meteorology"

NeurIPS 2020 SEVIR Code for paper: SEVIR : A Storm Event Imagery Dataset for Deep Learning Applications in Radar and Satellite Meteorology Requirement

USAF - MIT Artificial Intelligence Accelerator 46 Dec 15, 2022
Code for "Offline Meta-Reinforcement Learning with Advantage Weighting" [ICML 2021]

Offline Meta-Reinforcement Learning with Advantage Weighting (MACAW) MACAW code used for the experiments in the ICML 2021 paper. Installing the enviro

Eric Mitchell 28 Jan 01, 2023
Galileo library for large scale graph training by JD

近年来,图计算在搜索、推荐和风控等场景中获得显著的效果,但也面临超大规模异构图训练,与现有的深度学习框架Tensorflow和PyTorch结合等难题。 Galileo(伽利略)是一个图深度学习框架,具备超大规模、易使用、易扩展、高性能、双后端等优点,旨在解决超大规模图算法在工业级场景的落地难题,提

JD Galileo Team 128 Nov 29, 2022
Predictive Modeling on Electronic Health Records(EHR) using Pytorch

Predictive Modeling on Electronic Health Records(EHR) using Pytorch Overview Although there are plenty of repos on vision and NLP models, there are ve

81 Jan 01, 2023
Python library to receive live stream events like comments and gifts in realtime from TikTok LIVE.

TikTokLive A python library to connect to and read events from TikTok's LIVE service A python library to receive and decode livestream events such as

Isaac Kogan 277 Dec 23, 2022
[AI6122] Text Data Management & Processing

[AI6122] Text Data Management & Processing is an elective course of MSAI, SCSE, NTU, Singapore. The repository corresponds to the AI6122 of Semester 1, AY2021-2022, starting from 08/2021. The instruc

HT. Li 1 Jan 17, 2022
The fastai deep learning library

Welcome to fastai fastai simplifies training fast and accurate neural nets using modern best practices Important: This documentation covers fastai v2,

fast.ai 23.2k Jan 07, 2023
Camera Distortion-aware 3D Human Pose Estimation in Video with Optimization-based Meta-Learning

Camera Distortion-aware 3D Human Pose Estimation in Video with Optimization-based Meta-Learning This is the official repository of "Camera Distortion-

Hanbyel Cho 12 Oct 06, 2022
[CVPR 2022] Semi-Supervised Semantic Segmentation Using Unreliable Pseudo-Labels

Using Unreliable Pseudo Labels Official PyTorch implementation of Semi-Supervised Semantic Segmentation Using Unreliable Pseudo Labels, CVPR 2022. Ple

Haochen Wang 268 Dec 24, 2022
[Machine Learning Engineer Basic Guide] 부스트캠프 AI Tech - Product Serving 자료

Boostcamp-AI-Tech-Product-Serving 부스트캠프 AI Tech - Product Serving 자료 Repository 구조 part1(MLOps 개론, Model Serving, 머신러닝 프로젝트 라이프 사이클은 별도의 코드가 없으며, part

Sung Yun Byeon 269 Dec 21, 2022
Convert human motion from video to .bvh

video_to_bvh Convert human motion from video to .bvh with Google Colab Usage 1. Open video_to_bvh.ipynb in Google Colab Go to https://colab.research.g

Dene 306 Dec 10, 2022
Code for paper "Which Training Methods for GANs do actually Converge? (ICML 2018)"

GAN stability This repository contains the experiments in the supplementary material for the paper Which Training Methods for GANs do actually Converg

Lars Mescheder 885 Jan 01, 2023
My solution for the 7th place / 245 in the Umoja Hack 2022 challenge

Umoja Hack 2022 : Insurance Claim Challenge My solution for the 7th place / 245 in the Umoja Hack 2022 challenge Umoja Hack Africa is a yearly hackath

Souames Annis 17 Jun 03, 2022
A python software that can help blind people find things like laptops, phones, etc the same way a guide dog guides a blind person in finding his way.

GuidEye A python software that can help blind people find things like laptops, phones, etc the same way a guide dog guides a blind person in finding h

Munal Jain 0 Aug 09, 2022
An Active Automata Learning Library Written in Python

AALpy An Active Automata Learning Library AALpy is a light-weight active automata learning library written in pure Python. You can start learning auto

TU Graz - SAL Dependable Embedded Systems Lab (DES Lab) 78 Dec 30, 2022
This is an official implementation for "PlaneRecNet".

PlaneRecNet This is an official implementation for PlaneRecNet: A multi-task convolutional neural network provides instance segmentation for piece-wis

yaxu 50 Nov 17, 2022
Official implementation of Meta-StyleSpeech and StyleSpeech

Meta-StyleSpeech : Multi-Speaker Adaptive Text-to-Speech Generation Dongchan Min, Dong Bok Lee, Eunho Yang, and Sung Ju Hwang This is an official code

min95 168 Dec 28, 2022